{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 动手\n",
    "\n",
    "现在我们有2015到2017年25万条911的紧急电话的数据\n",
    "\n",
    "1. 请统计出出这些数据中不同类型的紧急情况的次数\n",
    "2. 如果我们还想统计出不同月份不同类型紧急电话的次数的变化情况(第2问也是下面`动手`的第2问)\n",
    "\n",
    "应该怎么做呢？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:10:52</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:29:21</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 14:39:21</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 16:47:36</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 16:56:52</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title            timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY  2015-12-10 17:10:52        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:29:21  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 14:39:21         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY  2015-12-10 16:47:36         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS  2015-12-10 16:56:52   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e  \n",
       "0      REINDEER CT & DEAD END  1  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1  \n",
       "2                    HAWS AVE  1  \n",
       "3          AIRY ST & SWEDE ST  1  \n",
       "4    CHERRYWOOD CT & DEAD END  1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./911.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 249737 entries, 0 to 249736\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   lat        249737 non-null  float64\n",
      " 1   lng        249737 non-null  float64\n",
      " 2   desc       249737 non-null  object \n",
      " 3   zip        219391 non-null  float64\n",
      " 4   title      249737 non-null  object \n",
      " 5   timeStamp  249737 non-null  object \n",
      " 6   twp        249644 non-null  object \n",
      " 7   addr       249737 non-null  object \n",
      " 8   e          249737 non-null  int64  \n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 17.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              [EMS, BACK PAINS/INJURY]\n",
       "1             [EMS, DIABETIC EMERGENCY]\n",
       "2                 [Fire, GAS-ODOR/LEAK]\n",
       "3              [EMS, CARDIAC EMERGENCY]\n",
       "4                      [EMS, DIZZINESS]\n",
       "                      ...              \n",
       "249732                  [EMS, OVERDOSE]\n",
       "249733               [EMS, FALL VICTIM]\n",
       "249734          [EMS, VEHICLE ACCIDENT]\n",
       "249735               [Fire, FIRE ALARM]\n",
       "249736    [Traffic, VEHICLE ACCIDENT -]\n",
       "Name: title, Length: 249737, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"title\"].str.split(': ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.请统计出出这些数据中不同类型的紧急情况的次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_list = df[\"title\"].str.split(': ').tolist()\n",
    "cate_list = list(set([i[0] for i in temp_list]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Fire', 'EMS', 'Traffic']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cate_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构造全为0的数组\n",
    "zeros_df = pd.DataFrame(np.zeros((df.shape[0],len(cate_list))),columns=cate_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>4</th>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>249733</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>249734</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>249735</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249736</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>249737 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fire  EMS  Traffic\n",
       "0        1.0  1.0      1.0\n",
       "1        1.0  1.0      1.0\n",
       "2        0.0  0.0      0.0\n",
       "3        1.0  1.0      1.0\n",
       "4        1.0  1.0      1.0\n",
       "...      ...  ...      ...\n",
       "249732   1.0  1.0      1.0\n",
       "249733   1.0  1.0      1.0\n",
       "249734   1.0  1.0      1.0\n",
       "249735   0.0  0.0      0.0\n",
       "249736   0.0  0.0      0.0\n",
       "\n",
       "[249737 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeros_df[df[\"title\"].str.contains(\"EMS\")] = 1\n",
    "zeros_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>249734</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>249735</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>249736</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "  </tbody>\n",
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       "<p>249737 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fire  EMS  Traffic\n",
       "0        1.0  1.0      1.0\n",
       "1        1.0  1.0      1.0\n",
       "2        1.0  1.0      1.0\n",
       "3        1.0  1.0      1.0\n",
       "4        1.0  1.0      1.0\n",
       "...      ...  ...      ...\n",
       "249732   1.0  1.0      1.0\n",
       "249733   1.0  1.0      1.0\n",
       "249734   1.0  1.0      1.0\n",
       "249735   1.0  1.0      1.0\n",
       "249736   1.0  1.0      1.0\n",
       "\n",
       "[249737 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 赋值\n",
    "for cate in cate_list:\n",
    "    zeros_df[df[\"title\"].str.contains(cate)] = 1\n",
    "zeros_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249735</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249736</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>249737 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fire  EMS  Traffic\n",
       "0        1.0  1.0      1.0\n",
       "1        1.0  1.0      1.0\n",
       "2        1.0  1.0      1.0\n",
       "3        1.0  1.0      1.0\n",
       "4        1.0  1.0      1.0\n",
       "...      ...  ...      ...\n",
       "249732   1.0  1.0      1.0\n",
       "249733   1.0  1.0      1.0\n",
       "249734   1.0  1.0      1.0\n",
       "249735   1.0  1.0      1.0\n",
       "249736   1.0  1.0      1.0\n",
       "\n",
       "[249737 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 另一种迭代方法赋值\n",
    "for i in range(df.shape[0]):\n",
    "    zeros_df.loc[i,temp_list[i][0]]= 1\n",
    "zeros_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**总结**: 第二种方法需要迭代`249737`次, 效率远远低于第一种迭代方法, 故不推荐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fire       249737.0\n",
       "EMS        249737.0\n",
       "Traffic    249737.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_ret = zeros_df.sum(axis=0)\n",
    "sum_ret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 但是我们可以使用分组得到相同的结果, 而不需要迭代"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>cate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:10:52</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:29:21</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 14:39:21</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>Fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 16:47:36</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 16:56:52</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>EMS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title            timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY  2015-12-10 17:10:52        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:29:21  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 14:39:21         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY  2015-12-10 16:47:36         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS  2015-12-10 16:56:52   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e  cate  \n",
       "0      REINDEER CT & DEAD END  1   EMS  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1   EMS  \n",
       "2                    HAWS AVE  1  Fire  \n",
       "3          AIRY ST & SWEDE ST  1   EMS  \n",
       "4    CHERRYWOOD CT & DEAD END  1   EMS  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cate_list = [i[0] for i in temp_list]\n",
    "df[\"cate\"] = pd.DataFrame(np.array(cate_list).reshape((df.shape[0],1)))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cate\n",
       "EMS        124840\n",
       "Fire        37432\n",
       "Traffic     87465\n",
       "Name: title, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by=\"cate\").count()[\"title\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 时间序列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',\n",
       "               '2017-01-05', '2017-01-06', '2017-01-07', '2017-01-08',\n",
       "               '2017-01-09', '2017-01-10',\n",
       "               ...\n",
       "               '2017-09-15', '2017-09-16', '2017-09-17', '2017-09-18',\n",
       "               '2017-09-19', '2017-09-20', '2017-09-21', '2017-09-22',\n",
       "               '2017-09-23', '2017-09-24'],\n",
       "              dtype='datetime64[ns]', length=267, freq='D')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20170101\", end=\"20170924\") #pd.date_range(start=\"20170101\", end=\"20170924\", freq=\"D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-01-01', '2017-01-11', '2017-01-21', '2017-01-31',\n",
       "               '2017-02-10', '2017-02-20', '2017-03-02', '2017-03-12',\n",
       "               '2017-03-22', '2017-04-01', '2017-04-11', '2017-04-21',\n",
       "               '2017-05-01', '2017-05-11', '2017-05-21', '2017-05-31',\n",
       "               '2017-06-10', '2017-06-20', '2017-06-30', '2017-07-10',\n",
       "               '2017-07-20', '2017-07-30', '2017-08-09', '2017-08-19',\n",
       "               '2017-08-29', '2017-09-08', '2017-09-18'],\n",
       "              dtype='datetime64[ns]', freq='10D')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20170101\", end=\"20170924\", freq=\"10D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04',\n",
       "               '2017-01-05', '2017-01-06', '2017-01-07', '2017-01-08',\n",
       "               '2017-01-09', '2017-01-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20170101\", periods=10, freq=\"D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-01-31', '2017-02-28', '2017-03-31', '2017-04-28',\n",
       "               '2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31'],\n",
       "              dtype='datetime64[ns]', freq='BM')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20170101\", end=\"20170924\", freq=\"BM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-01-15', '2021-02-19'], dtype='datetime64[ns]', freq='WOM-3FRI')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20210101\", periods=2, freq=\"WOM-3FRI\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面这个写法的意思参见网址：https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-periods\n",
    "- `WOM`: the x-th day of the y-th week of each month\n",
    "- `3FRI`\n",
    "    - `3`第三周\n",
    "    - `FRI`星期五\n",
    "    - `3FRI`第三周的星期五"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 重采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>15.833973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>21.538210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>43.209721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>19.409468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>19.693852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-27</th>\n",
       "      <td>10.541691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-28</th>\n",
       "      <td>39.622317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-29</th>\n",
       "      <td>39.583622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-30</th>\n",
       "      <td>17.355284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>48.317056</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>700 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0\n",
       "2017-01-01  15.833973\n",
       "2017-01-02  21.538210\n",
       "2017-01-03  43.209721\n",
       "2017-01-04  19.409468\n",
       "2017-01-05  19.693852\n",
       "...               ...\n",
       "2018-11-27  10.541691\n",
       "2018-11-28  39.622317\n",
       "2018-11-29  39.583622\n",
       "2018-11-30  17.355284\n",
       "2018-12-01  48.317056\n",
       "\n",
       "[700 rows x 1 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = pd.DataFrame(\n",
    "    np.random.uniform(10,50,(700,1)),\n",
    "    index=pd.date_range(\"20170101\",periods=700)\n",
    ")\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>32.669281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-28</th>\n",
       "      <td>31.661225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-31</th>\n",
       "      <td>27.267201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-30</th>\n",
       "      <td>27.638032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0\n",
       "2017-01-31  32.669281\n",
       "2017-02-28  31.661225\n",
       "2017-03-31  27.267201\n",
       "2017-04-30  27.638032"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.resample(\"M\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-21</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-10</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-13</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-23</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-12</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-22</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0\n",
       "2017-01-01  10\n",
       "2017-01-11  10\n",
       "2017-01-21  10\n",
       "2017-01-31  10\n",
       "2017-02-10  10\n",
       "...         ..\n",
       "2018-10-13  10\n",
       "2018-10-23  10\n",
       "2018-11-02  10\n",
       "2018-11-12  10\n",
       "2018-11-22  10\n",
       "\n",
       "[70 rows x 1 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.resample(\"10D\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2017-01-01</th>\n",
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       "2018-04-01  91\n",
       "2018-07-01  92\n",
       "2018-10-01  62"
      ]
     },
     "execution_count": 34,
     "metadata": {},
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    }
   ],
   "source": [
    "t.resample(\"QS-JAN\").count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 动手\n",
    "\n",
    "1. 统计出911数据中不同月份电话次数的变化情况\n",
    "2. 统计出911数据中不同月份不同类型的电话的次数的变化情况\n",
    "\n",
    "### 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timeStamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-10 17:10:52</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 17:29:21</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 14:39:21</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 16:47:36</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10 16:56:52</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           lat        lng  \\\n",
       "timeStamp                                   \n",
       "2015-12-10 17:10:52  40.297876 -75.581294   \n",
       "2015-12-10 17:29:21  40.258061 -75.264680   \n",
       "2015-12-10 14:39:21  40.121182 -75.351975   \n",
       "2015-12-10 16:47:36  40.116153 -75.343513   \n",
       "2015-12-10 16:56:52  40.251492 -75.603350   \n",
       "\n",
       "                                                                  desc  \\\n",
       "timeStamp                                                                \n",
       "2015-12-10 17:10:52  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "2015-12-10 17:29:21  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2015-12-10 14:39:21  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "2015-12-10 16:47:36  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "2015-12-10 16:56:52  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "                         zip                    title                twp  \\\n",
       "timeStamp                                                                  \n",
       "2015-12-10 17:10:52  19525.0   EMS: BACK PAINS/INJURY        NEW HANOVER   \n",
       "2015-12-10 17:29:21  19446.0  EMS: DIABETIC EMERGENCY  HATFIELD TOWNSHIP   \n",
       "2015-12-10 14:39:21  19401.0      Fire: GAS-ODOR/LEAK         NORRISTOWN   \n",
       "2015-12-10 16:47:36  19401.0   EMS: CARDIAC EMERGENCY         NORRISTOWN   \n",
       "2015-12-10 16:56:52      NaN           EMS: DIZZINESS   LOWER POTTSGROVE   \n",
       "\n",
       "                                           addr  e  \n",
       "timeStamp                                           \n",
       "2015-12-10 17:10:52      REINDEER CT & DEAD END  1  \n",
       "2015-12-10 17:29:21  BRIAR PATH & WHITEMARSH LN  1  \n",
       "2015-12-10 14:39:21                    HAWS AVE  1  \n",
       "2015-12-10 16:47:36          AIRY ST & SWEDE ST  1  \n",
       "2015-12-10 16:56:52    CHERRYWOOD CT & DEAD END  1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./911.csv')\n",
    "df[\"timeStamp\"] = pd.to_datetime(df[\"timeStamp\"])\n",
    "df.set_index(\"timeStamp\",inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timeStamp\n",
       "2015-12-31     7916\n",
       "2016-01-31    13096\n",
       "2016-02-29    11396\n",
       "2016-03-31    11059\n",
       "2016-04-30    11287\n",
       "2016-05-31    11374\n",
       "2016-06-30    11732\n",
       "2016-07-31    12088\n",
       "2016-08-31    11904\n",
       "2016-09-30    11669\n",
       "2016-10-31    12502\n",
       "2016-11-30    12091\n",
       "2016-12-31    12162\n",
       "2017-01-31    11605\n",
       "2017-02-28    10267\n",
       "2017-03-31    11684\n",
       "2017-04-30    11056\n",
       "2017-05-31    11719\n",
       "2017-06-30    12333\n",
       "2017-07-31    11768\n",
       "2017-08-31    11753\n",
       "2017-09-30     7276\n",
       "Freq: M, Name: title, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_by_month = df.resample(\"M\").count()[\"title\"]\n",
    "count_by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 画图\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "_x = count_by_month.index\n",
    "_y = count_by_month.values\n",
    "plt.plot(range(len(_x)),_y)\n",
    "plt.xticks(range(len(_x)), _x, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015-12-31 00:00:00 ['__add__', '__array_priority__', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__pyx_vtable__', '__radd__', '__reduce__', '__reduce_cython__', '__reduce_ex__', '__repr__', '__rsub__', '__setattr__', '__setstate__', '__setstate_cython__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__weakref__', '_date_repr', '_repr_base', '_round', '_short_repr', '_time_repr', 'asm8', 'astimezone', 'ceil', 'combine', 'ctime', 'date', 'day', 'day_name', 'day_of_week', 'day_of_year', 'dayofweek', 'dayofyear', 'days_in_month', 'daysinmonth', 'dst', 'floor', 'fold', 'freq', 'freqstr', 'fromisoformat', 'fromordinal', 'fromtimestamp', 'hour', 'is_leap_year', 'is_month_end', 'is_month_start', 'is_quarter_end', 'is_quarter_start', 'is_year_end', 'is_year_start', 'isocalendar', 'isoformat', 'isoweekday', 'max', 'microsecond', 'min', 'minute', 'month', 'month_name', 'nanosecond', 'normalize', 'now', 'quarter', 'replace', 'resolution', 'round', 'second', 'strftime', 'strptime', 'time', 'timestamp', 'timetuple', 'timetz', 'to_datetime64', 'to_julian_date', 'to_numpy', 'to_period', 'to_pydatetime', 'today', 'toordinal', 'tz', 'tz_convert', 'tz_localize', 'tzinfo', 'tzname', 'utcfromtimestamp', 'utcnow', 'utcoffset', 'utctimetuple', 'value', 'week', 'weekday', 'weekofyear', 'year']\n"
     ]
    }
   ],
   "source": [
    "print(_x[0],dir(_x[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "_x = count_by_month.index\n",
    "_x = [i.strftime(\"%Y-%m-%d\") for i in _x]\n",
    "_y = count_by_month.values\n",
    "plt.plot(range(len(_x)),_y)\n",
    "plt.xticks(range(len(_x)), _x, rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./911.csv')\n",
    "\n",
    "# 1.添加列,表示分类\n",
    "temp_list = df[\"title\"].str.split(\": \").tolist()\n",
    "cate_list = [i[0] for i in temp_list]\n",
    "df[\"cate\"] = pd.DataFrame(np.array(cate_list).reshape((df.shape[0],1)))\n",
    "'''错误写法: df[\"cate\"] = pd.DataFrame(np.array(cate_list).reshape((df.shape[0],1)),columns=\"cate\")'''\n",
    "\n",
    "# 2.把时间字符串转为时间类型 并 设置为索引\n",
    "df[\"timeStamp\"] = pd.to_datetime(df[\"timeStamp\"])\n",
    "df.set_index(\"timeStamp\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3.分组并绘图\n",
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "\n",
    "for group_name,group_data in df.groupby(by=\"cate\"):\n",
    "    count_by_month = group_data.resample(\"M\").count()[\"title\"]\n",
    "    _x = count_by_month.index\n",
    "    _x = [i.strftime(\"%Y-%m-%d\") for i in _x]\n",
    "    _y = count_by_month.values\n",
    "    plt.plot(range(len(_x)),_y,label=group_name)\n",
    "    \n",
    "plt.xticks(range(len(_x)), _x, rotation=45)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**注意**:\n",
    "\n",
    "第一步必须在第二步之前,原因是第二步中重新设置了索引,若第2步在前,会导致添加一列之后,由于添加列的索引与重新设置的索引不一致,而导致添加之后df[\"cate\"]全部为NaN"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 动手\n",
    "\n",
    "现在我们有北上广、深圳、和沈阳5个城市空气质量数据，请绘制出5个城市的PM2.5随时间的变化情况\n",
    "\n",
    "观察这组数据中的时间结构，并不是字符串，这个时候我们应该怎么办？\n",
    "\n",
    "数据来源： https://www.kaggle.com/uciml/pm25-data-for-five-chinese-cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>season</th>\n",
       "      <th>PM_Dongsi</th>\n",
       "      <th>PM_Dongsihuan</th>\n",
       "      <th>PM_Nongzhanguan</th>\n",
       "      <th>PM_US Post</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>HUMI</th>\n",
       "      <th>PRES</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>cbwd</th>\n",
       "      <th>Iws</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>Iprec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>1.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>4.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>6.71</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>12.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No  year  month  day  hour  season  PM_Dongsi  PM_Dongsihuan  \\\n",
       "0   1  2010      1    1     0       4        NaN            NaN   \n",
       "1   2  2010      1    1     1       4        NaN            NaN   \n",
       "2   3  2010      1    1     2       4        NaN            NaN   \n",
       "3   4  2010      1    1     3       4        NaN            NaN   \n",
       "4   5  2010      1    1     4       4        NaN            NaN   \n",
       "\n",
       "   PM_Nongzhanguan  PM_US Post  DEWP  HUMI    PRES  TEMP cbwd    Iws  \\\n",
       "0              NaN         NaN -21.0  43.0  1021.0 -11.0   NW   1.79   \n",
       "1              NaN         NaN -21.0  47.0  1020.0 -12.0   NW   4.92   \n",
       "2              NaN         NaN -21.0  43.0  1019.0 -11.0   NW   6.71   \n",
       "3              NaN         NaN -21.0  55.0  1019.0 -14.0   NW   9.84   \n",
       "4              NaN         NaN -20.0  51.0  1018.0 -12.0   NW  12.97   \n",
       "\n",
       "   precipitation  Iprec  \n",
       "0            0.0    0.0  \n",
       "1            0.0    0.0  \n",
       "2            0.0    0.0  \n",
       "3            0.0    0.0  \n",
       "4            0.0    0.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = './PM2.5/BeijingPM20100101_20151231.csv'\n",
    "df=pd.read_csv(file_path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 52584 entries, 0 to 52583\n",
      "Data columns (total 18 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   No               52584 non-null  int64  \n",
      " 1   year             52584 non-null  int64  \n",
      " 2   month            52584 non-null  int64  \n",
      " 3   day              52584 non-null  int64  \n",
      " 4   hour             52584 non-null  int64  \n",
      " 5   season           52584 non-null  int64  \n",
      " 6   PM_Dongsi        25052 non-null  float64\n",
      " 7   PM_Dongsihuan    20508 non-null  float64\n",
      " 8   PM_Nongzhanguan  24931 non-null  float64\n",
      " 9   PM_US Post       50387 non-null  float64\n",
      " 10  DEWP             52579 non-null  float64\n",
      " 11  HUMI             52245 non-null  float64\n",
      " 12  PRES             52245 non-null  float64\n",
      " 13  TEMP             52579 non-null  float64\n",
      " 14  cbwd             52579 non-null  object \n",
      " 15  Iws              52579 non-null  float64\n",
      " 16  precipitation    52100 non-null  float64\n",
      " 17  Iprec            52100 non-null  float64\n",
      "dtypes: float64(11), int64(6), object(1)\n",
      "memory usage: 7.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2010-01-01 00:00', '2010-01-01 01:00', '2010-01-01 02:00',\n",
       "             '2010-01-01 03:00', '2010-01-01 04:00', '2010-01-01 05:00',\n",
       "             '2010-01-01 06:00', '2010-01-01 07:00', '2010-01-01 08:00',\n",
       "             '2010-01-01 09:00',\n",
       "             ...\n",
       "             '2015-12-31 14:00', '2015-12-31 15:00', '2015-12-31 16:00',\n",
       "             '2015-12-31 17:00', '2015-12-31 18:00', '2015-12-31 19:00',\n",
       "             '2015-12-31 20:00', '2015-12-31 21:00', '2015-12-31 22:00',\n",
       "             '2015-12-31 23:00'],\n",
       "            dtype='period[H]', length=52584, freq='H')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 把分开的时间字符串通过PeriodIndex方法转换为pandas的时间类型\n",
    "periods = pd.PeriodIndex(year=df[\"year\"], month=df[\"month\"], day=df[\"day\"], hour=df[\"hour\"], freq=\"H\")\n",
    "periods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.period.PeriodIndex"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(periods)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> PeriodIndex 和 DatetimeIndex 唯一的区别在于构造方法不一样，其他的都一样"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制北京市PM2.5中美发布数据对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>season</th>\n",
       "      <th>PM_Dongsi</th>\n",
       "      <th>PM_Dongsihuan</th>\n",
       "      <th>PM_Nongzhanguan</th>\n",
       "      <th>PM_US Post</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>HUMI</th>\n",
       "      <th>PRES</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>cbwd</th>\n",
       "      <th>Iws</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>Iprec</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01 00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>1.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-01 00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 01:00</th>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>4.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-01 01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 02:00</th>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>6.71</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-01 02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 03:00</th>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-01 03:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01 04:00</th>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>12.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-01 04:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  No  year  month  day  hour  season  PM_Dongsi  \\\n",
       "datetime                                                          \n",
       "2010-01-01 00:00   1  2010      1    1     0       4        NaN   \n",
       "2010-01-01 01:00   2  2010      1    1     1       4        NaN   \n",
       "2010-01-01 02:00   3  2010      1    1     2       4        NaN   \n",
       "2010-01-01 03:00   4  2010      1    1     3       4        NaN   \n",
       "2010-01-01 04:00   5  2010      1    1     4       4        NaN   \n",
       "\n",
       "                  PM_Dongsihuan  PM_Nongzhanguan  PM_US Post  DEWP  HUMI  \\\n",
       "datetime                                                                   \n",
       "2010-01-01 00:00            NaN              NaN         NaN -21.0  43.0   \n",
       "2010-01-01 01:00            NaN              NaN         NaN -21.0  47.0   \n",
       "2010-01-01 02:00            NaN              NaN         NaN -21.0  43.0   \n",
       "2010-01-01 03:00            NaN              NaN         NaN -21.0  55.0   \n",
       "2010-01-01 04:00            NaN              NaN         NaN -20.0  51.0   \n",
       "\n",
       "                    PRES  TEMP cbwd    Iws  precipitation  Iprec  \\\n",
       "datetime                                                           \n",
       "2010-01-01 00:00  1021.0 -11.0   NW   1.79            0.0    0.0   \n",
       "2010-01-01 01:00  1020.0 -12.0   NW   4.92            0.0    0.0   \n",
       "2010-01-01 02:00  1019.0 -11.0   NW   6.71            0.0    0.0   \n",
       "2010-01-01 03:00  1019.0 -14.0   NW   9.84            0.0    0.0   \n",
       "2010-01-01 04:00  1018.0 -12.0   NW  12.97            0.0    0.0   \n",
       "\n",
       "                          datetime  \n",
       "datetime                            \n",
       "2010-01-01 00:00  2010-01-01 00:00  \n",
       "2010-01-01 01:00  2010-01-01 01:00  \n",
       "2010-01-01 02:00  2010-01-01 02:00  \n",
       "2010-01-01 03:00  2010-01-01 03:00  \n",
       "2010-01-01 04:00  2010-01-01 04:00  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"datetime\"] = periods\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把datetime设置为索引\n",
    "df.set_index(\"datetime\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(313, 17)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 降采样\n",
    "df = df.resample(\"7D\").mean()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 处理缺失数据（删除）\n",
    "us_data = df[\"PM_US Post\"].dropna()\n",
    "cn_data = df[\"PM_Dongsi\"].dropna()\n",
    "\n",
    "# 画图\n",
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "\n",
    "_x_us = us_data.index\n",
    "_x_us = [i.strftime(\"%Y-%m-%d\") for i in _x_us]\n",
    "_y_us = us_data.values\n",
    "_x_cn = cn_data.index\n",
    "_x_cn = [i.strftime(\"%Y-%m-%d\") for i in _x_cn]\n",
    "_y_cn = cn_data.values\n",
    "\n",
    "plt.plot(_x_us, _y_us, label=\"US_POST\")\n",
    "plt.plot(_x_cn, _y_cn, label=\"CN_POST\")\n",
    "plt.xticks(range(0,len(_x_us),10), _x_us[::10], rotation=45) #根据降采样后的df.shape[0],我们取10 的步长\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> 中国没有发布前半段时间的数据（全为NaN），所以最终结果呈现出上图的样子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:analyze]",
   "language": "python",
   "name": "conda-env-analyze-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
